我有一个带有架构的数据框
root
|-- x: Long (nullable = false)
|-- y: Long (nullable = false)
|-- features: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- name: string (nullable = true)
| | |-- score: double (nullable = true)
例如,我有数据
+--------------------+--------------------+------------------------------------------+
| x | y | features |
+--------------------+--------------------+------------------------------------------+
|10 | 9 |[["f1", 5.9], ["ft2", 6.0], ["ft3", 10.9]]|
|11 | 0 |[["f4", 0.9], ["ft1", 4.0], ["ft2", 0.9] ]|
|20 | 9 |[["f5", 5.9], ["ft2", 6.4], ["ft3", 1.9] ]|
|18 | 8 |[["f1", 5.9], ["ft4", 8.1], ["ft2", 18.9]]|
+--------------------+--------------------+------------------------------------------+
我想使用特定的前缀(例如“ ft”)过滤功能,因此最终我需要结果:
+--------------------+--------------------+-----------------------------+
| x | y | features |
+--------------------+--------------------+-----------------------------+
|10 | 9 |[["ft2", 6.0], ["ft3", 10.9]]|
|11 | 0 |[["ft1", 4.0], ["ft2", 0.9] ]|
|20 | 9 |[["ft2", 6.4], ["ft3", 1.9] ]|
|18 | 8 |[["ft4", 8.1], ["ft2", 18.9]]|
+--------------------+--------------------+-----------------------------+
我未使用Spark 2.4+,因此无法使用此处提供的解决方案:Spark(Scala)过滤器结构数组未爆炸
我尝试使用UDF,但仍然无法正常工作。这是我的尝试。我定义一个UDF:
def filterFeature: UserDefinedFunction =
udf((features: Seq[Row]) =>
features.filter{
x.getString(0).startsWith("ft")
}
)
但是如果我应用这个UDF
df.withColumn("filtered", filterFeature($"features"))
我得到了错误Schema for type org.apache.spark.sql.Row is not supported
。我发现我无法Row
从UDF 返回。然后我尝试
def filterFeature: UserDefinedFunction =
udf((features: Seq[Row]) =>
features.filter{
x.getString(0).startsWith("ft")
}, (StringType, DoubleType)
)
然后我得到一个错误:
error: type mismatch;
found : (org.apache.spark.sql.types.StringType.type, org.apache.spark.sql.types.DoubleType.type)
required: org.apache.spark.sql.types.DataType
}, (StringType, DoubleType)
^
我还尝试了一些答案所建议的案例类:
case class FilteredFeature(featureName: String, featureScore: Double)
def filterFeature: UserDefinedFunction =
udf((features: Seq[Row]) =>
features.filter{
x.getString(0).startsWith("ft")
}, FilteredFeature
)
但是我得到了:
error: type mismatch;
found : FilteredFeature.type
required: org.apache.spark.sql.types.DataType
}, FilteredFeature
^
我试过了:
case class FilteredFeature(featureName: String, featureScore: Double)
def filterFeature: UserDefinedFunction =
udf((features: Seq[Row]) =>
features.filter{
x.getString(0).startsWith("ft")
}, Seq[FilteredFeature]
)
我有:
<console>:192: error: missing argument list for method apply in class GenericCompanion
Unapplied methods are only converted to functions when a function type is expected.
You can make this conversion explicit by writing `apply _` or `apply(_)` instead of `apply`.
}, Seq[FilteredFeature]
^
我试过了:
case class FilteredFeature(featureName: String, featureScore: Double)
def filterFeature: UserDefinedFunction =
udf((features: Seq[Row]) =>
features.filter{
x.getString(0).startsWith("ft")
}, Seq[FilteredFeature](_)
)
我有:
<console>:201: error: type mismatch;
found : Seq[FilteredFeature]
required: FilteredFeature
}, Seq[FilteredFeature](_)
^
在这种情况下我该怎么办?
您有两个选择:
a)为UDF提供一个模式,让您返回 Seq[Row]
b)转换Seq[Row]
为Seq
of Tuple2
或case类,则无需提供架构(但是如果使用Tuples,则结构字段名称将会丢失!)
我希望选项a)适合您的情况(适用于具有许多字段的结构):
val schema = df.schema("features").dataType
val filterFeature = udf((features:Seq[Row]) => features.filter(_.getAs[String]("name").startsWith("ft")),schema)